Terms, Topics & Tasks: Enhanced User Modelling for Better Personalization

Abstract

Given the distinct preferences of different users while using search engines, search personalization has become an important problem in information retrieval. Most approaches to search personalization are based on identifying topics a user may be interested in and personalizing search results based on this information. While topical interests information of users can be highly valuable in personalizing search results and improving user experience, it ignores the fact that two different users that have similar topical interests may still be interested in achieving very different tasks with respect to this topic (e.g. the type of tasks a broker is likely to perform related to finance is likely to be very different than that of a regular investor). Hence, considering user's topical interests jointly with the type of tasks they are likely to be interested in could result in better personalised We present an approach that uses search task information embedded in search logs to represent users by their actions over a task-space as well as over their topical-interest space. In particular, we describe a tensor based approach that represents each user in terms of (i) user's topical interests and (ii) user's search task behaviours in a coupled fashion and use these representations for personalization. Additionally, we also integrate user's historic search behavior in a coupled matrix-tensor factorization framework to learn user representations. Through extensive evaluation via query recommendations and user cohort analysis, we demonstrate the value of considering topic specific task information while developing user models.